This notebook processes level 2 images through the calwebb_image3 skymatch and resample steps and examines outputs for different sky method parameters.
Set up data path and image list file.
Set up association files.
Modify average backgrould level of input images.
Run skymatch step on images.
Run skymatch and resample for each skymethod parameter (local, global, match, global+match).
Testing other parameters (nclip, usigma, lsigma, lower, upper, skystat). (Still TBD.)
These steps are set up with simulated MIRI F560W data of the LMC astrometric field. The notebook removes the astrometric field and replaces the field with noise images to help the pipeline testers have a better visualization of what the pipeline is doing with each set of parameters.
This notebook has also been modified to test how skymatch and resample work together (with subtract=True indicating that the subtraction is done in the skymatch step and subtract=False indicating that the subtraction is done in the resample step). There are displays of the combined noise image to see how well the backgrounds were subtracted in the full combined image.
The pipeline documentation can be found here: https://jwst-pipeline.readthedocs.io/en/latest/jwst/skymatch/README.html
The pipeline code is available on GitHub: https://github.com/spacetelescope/jwst/tree/master/jwst/skymatch
Authors: T. Temim and M. Cracraft Last modified: 01/18/2022
# Create a temporary directory to hold notebook output, and change the working directory to that directory.
from tempfile import TemporaryDirectory
import os
data_dir = TemporaryDirectory()
os.chdir(data_dir.name)
# Set up CRDS options
import os
if "CRDS_CACHE_TYPE" in os.environ:
if os.environ['CRDS_CACHE_TYPE'] == 'local':
os.environ['CRDS_PATH'] = os.path.join(os.environ['HOME'], 'crds', 'cache')
elif os.path.isdir(os.environ['CRDS_CACHE_TYPE']):
os.environ['CRDS_PATH'] = os.environ['CRDS_CACHE_TYPE']